Overview

Dataset statistics

Number of variables26
Number of observations17925
Missing cells86569
Missing cells (%)18.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory208.0 B

Variable types

Categorical12
Numeric14

Warnings

has_deposit is highly correlated with is_convertedHigh correlation
mifid_money_other_brokers is highly correlated with mifid_invested_other_brokersHigh correlation
mifid_invested_other_brokers is highly correlated with mifid_money_other_brokersHigh correlation
is_converted is highly correlated with has_depositHigh correlation
is_converted is highly correlated with has_depositHigh correlation
has_deposit is highly correlated with is_convertedHigh correlation
finish_mifid_days has 7513 (41.9%) missing values Missing
first_deposit_days has 12891 (71.9%) missing values Missing
linked_account_days has 17300 (96.5%) missing values Missing
demo_account_days has 15221 (84.9%) missing values Missing
demo_trade_days has 15787 (88.1%) missing values Missing
mock_account_days has 17848 (99.6%) missing values Missing
first_deposit_amount is highly skewed (γ1 = 27.49295343) Skewed
start_mifid_days has 12155 (67.8%) zeros Zeros
finish_mifid_days has 2219 (12.4%) zeros Zeros
first_deposit_days has 204 (1.1%) zeros Zeros
first_deposit_amount has 12891 (71.9%) zeros Zeros
first_deposit_platform has 1915 (10.7%) zeros Zeros
mifid_actual_savings has 1543 (8.6%) zeros Zeros
mifid_next_year_savings has 1543 (8.6%) zeros Zeros
mifid_invested_other_brokers has 8241 (46.0%) zeros Zeros
linked_account_days has 301 (1.7%) zeros Zeros
demo_account_days has 2056 (11.5%) zeros Zeros
demo_trade_days has 606 (3.4%) zeros Zeros

Reproduction

Analysis started2021-05-31 14:08:45.586657
Analysis finished2021-05-31 14:09:46.038749
Duration1 minute and 0.45 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

user_currency
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
USD
10675 
EUR
6405 
GBP
 
842
NO_CURRENCY
 
3

Length

Max length11
Median length3
Mean length3.001338912
Min length3

Characters and Unicode

Total characters53799
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUR
2nd rowUSD
3rd rowEUR
4th rowUSD
5th rowUSD
ValueCountFrequency (%)
USD10675
59.6%
EUR6405
35.7%
GBP842
 
4.7%
NO_CURRENCY3
 
< 0.1%
2021-05-31T16:09:46.449715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:46.632716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
usd10675
59.6%
eur6405
35.7%
gbp842
 
4.7%
no_currency3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U17083
31.8%
S10675
19.8%
D10675
19.8%
R6411
 
11.9%
E6408
 
11.9%
G842
 
1.6%
B842
 
1.6%
P842
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter53796
> 99.9%
Connector Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
U17083
31.8%
S10675
19.8%
D10675
19.8%
R6411
 
11.9%
E6408
 
11.9%
G842
 
1.6%
B842
 
1.6%
P842
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (2)6
 
< 0.1%
ValueCountFrequency (%)
_3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53796
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
U17083
31.8%
S10675
19.8%
D10675
19.8%
R6411
 
11.9%
E6408
 
11.9%
G842
 
1.6%
B842
 
1.6%
P842
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (2)6
 
< 0.1%
ValueCountFrequency (%)
_3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53799
100.0%

Most frequent character per block

ValueCountFrequency (%)
U17083
31.8%
S10675
19.8%
D10675
19.8%
R6411
 
11.9%
E6408
 
11.9%
G842
 
1.6%
B842
 
1.6%
P842
 
1.6%
N6
 
< 0.1%
C6
 
< 0.1%
Other values (3)9
 
< 0.1%

user_country
Real number (ℝ≥0)

Distinct162
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.30945607
Minimum0
Maximum161
Zeros31
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:46.863766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q132
median46
Q3100
95-th percentile160
Maximum161
Range161
Interquartile range (IQR)68

Descriptive statistics

Standard deviation44.52194711
Coefficient of variation (CV)0.6714268182
Kurtosis-0.4134508841
Mean66.30945607
Median Absolute Deviation (MAD)16
Skewness0.8752028595
Sum1188597
Variance1982.203774
MonotonicityNot monotonic
2021-05-31T16:09:47.161141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464426
24.7%
311232
 
6.9%
531101
 
6.1%
1601096
 
6.1%
100963
 
5.4%
8771
 
4.3%
30569
 
3.2%
111531
 
3.0%
28450
 
2.5%
43406
 
2.3%
Other values (152)6380
35.6%
ValueCountFrequency (%)
031
 
0.2%
1100
0.6%
21
 
< 0.1%
31
 
< 0.1%
47
 
< 0.1%
ValueCountFrequency (%)
161298
 
1.7%
1601096
6.1%
1593
 
< 0.1%
1581
 
< 0.1%
1576
 
< 0.1%

start_mifid_days
Real number (ℝ≥0)

ZEROS

Distinct583
Distinct (%)3.3%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean25.62006028
Minimum0
Maximum1112
Zeros12155
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:47.475807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile163
Maximum1112
Range1112
Interquartile range (IQR)3

Descriptive statistics

Standard deviation87.71987573
Coefficient of variation (CV)3.423874681
Kurtosis34.80838146
Mean25.62006028
Median Absolute Deviation (MAD)0
Skewness5.347735651
Sum459009
Variance7694.776598
MonotonicityNot monotonic
2021-05-31T16:09:47.776947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012155
67.8%
1849
 
4.7%
2367
 
2.0%
3248
 
1.4%
4230
 
1.3%
5141
 
0.8%
7135
 
0.8%
6120
 
0.7%
8107
 
0.6%
1183
 
0.5%
Other values (573)3481
 
19.4%
ValueCountFrequency (%)
012155
67.8%
1849
 
4.7%
2367
 
2.0%
3248
 
1.4%
4230
 
1.3%
ValueCountFrequency (%)
11121
< 0.1%
10791
< 0.1%
10331
< 0.1%
10191
< 0.1%
9801
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
1
10411 
0
7514 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
110411
58.1%
07514
41.9%
2021-05-31T16:09:48.288992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:48.435947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110411
58.1%
07514
41.9%

Most occurring characters

ValueCountFrequency (%)
110411
58.1%
07514
41.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
110411
58.1%
07514
41.9%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
110411
58.1%
07514
41.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
110411
58.1%
07514
41.9%

finish_mifid_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct579
Distinct (%)5.6%
Missing7513
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean43.13983865
Minimum0
Maximum1114
Zeros2219
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:48.633985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q321
95-th percentile264.45
Maximum1114
Range1114
Interquartile range (IQR)20

Descriptive statistics

Standard deviation111.2103772
Coefficient of variation (CV)2.577904338
Kurtosis20.15001096
Mean43.13983865
Median Absolute Deviation (MAD)3
Skewness4.113110221
Sum449172
Variance12367.748
MonotonicityNot monotonic
2021-05-31T16:09:48.930946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02219
 
12.4%
12017
 
11.3%
2897
 
5.0%
3566
 
3.2%
4366
 
2.0%
5274
 
1.5%
6206
 
1.1%
7182
 
1.0%
8146
 
0.8%
9101
 
0.6%
Other values (569)3438
19.2%
(Missing)7513
41.9%
ValueCountFrequency (%)
02219
12.4%
12017
11.3%
2897
5.0%
3566
 
3.2%
4366
 
2.0%
ValueCountFrequency (%)
11141
< 0.1%
10851
< 0.1%
10331
< 0.1%
10191
< 0.1%
9801
< 0.1%

has_deposit
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
12891 
1
5034 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%
2021-05-31T16:09:49.490983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:49.648947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%

Most occurring characters

ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
012891
71.9%
15034
 
28.1%

first_deposit_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct539
Distinct (%)10.7%
Missing12891
Missing (%)71.9%
Infinite0
Infinite (%)0.0%
Mean79.81466031
Minimum0
Maximum1105
Zeros204
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:49.847948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median18
Q382
95-th percentile392
Maximum1105
Range1105
Interquartile range (IQR)77

Descriptive statistics

Standard deviation142.119085
Coefficient of variation (CV)1.780613792
Kurtosis9.811300548
Mean79.81466031
Median Absolute Deviation (MAD)16
Skewness2.911893368
Sum401787
Variance20197.83431
MonotonicityNot monotonic
2021-05-31T16:09:50.236991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1287
 
1.6%
3247
 
1.4%
2241
 
1.3%
0204
 
1.1%
4197
 
1.1%
6184
 
1.0%
5176
 
1.0%
7148
 
0.8%
8118
 
0.7%
9115
 
0.6%
Other values (529)3117
 
17.4%
(Missing)12891
71.9%
ValueCountFrequency (%)
0204
1.1%
1287
1.6%
2241
1.3%
3247
1.4%
4197
1.1%
ValueCountFrequency (%)
11051
< 0.1%
10851
< 0.1%
10441
< 0.1%
10191
< 0.1%
9831
< 0.1%

first_deposit_amount
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct670
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.907412657
Minimum0
Maximum1000
Zeros12891
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:50.527986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile12
Maximum1000
Range1000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation17.78616558
Coefficient of variation (CV)6.117523612
Kurtosis1163.285926
Mean2.907412657
Median Absolute Deviation (MAD)0
Skewness27.49295343
Sum52115.37188
Variance316.3476861
MonotonicityNot monotonic
2021-05-31T16:09:50.837948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012891
71.9%
21748
 
9.8%
4875
 
4.9%
8258
 
1.4%
20197
 
1.1%
2.4131
 
0.7%
40123
 
0.7%
12121
 
0.7%
6101
 
0.6%
1068
 
0.4%
Other values (660)1412
 
7.9%
ValueCountFrequency (%)
012891
71.9%
0.011161
 
< 0.1%
0.021921
 
< 0.1%
0.0281
 
< 0.1%
0.030361
 
< 0.1%
ValueCountFrequency (%)
10001
< 0.1%
879.7541
< 0.1%
739.381
< 0.1%
4002
< 0.1%
399.9741
< 0.1%

first_deposit_platform
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.468005579
Minimum0
Maximum6
Zeros1915
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:51.085987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.395446839
Coefficient of variation (CV)0.9505732536
Kurtosis2.009885083
Mean1.468005579
Median Absolute Deviation (MAD)0
Skewness1.785047765
Sum26314
Variance1.947271881
MonotonicityNot monotonic
2021-05-31T16:09:51.286764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
112891
71.9%
01915
 
10.7%
51683
 
9.4%
3906
 
5.1%
4387
 
2.2%
6114
 
0.6%
229
 
0.2%
ValueCountFrequency (%)
01915
 
10.7%
112891
71.9%
229
 
0.2%
3906
 
5.1%
4387
 
2.2%
ValueCountFrequency (%)
6114
 
0.6%
51683
9.4%
4387
 
2.2%
3906
5.1%
229
 
0.2%

mifid_actual_savings
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.921562064
Minimum0
Maximum15
Zeros1543
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:51.498763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median10
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.994764177
Coefficient of variation (CV)0.4477651053
Kurtosis-0.373526453
Mean8.921562064
Median Absolute Deviation (MAD)3
Skewness-0.7635030379
Sum159919
Variance15.95814083
MonotonicityNot monotonic
2021-05-31T16:09:51.709808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
125823
32.5%
132772
15.5%
52107
 
11.8%
61682
 
9.4%
71606
 
9.0%
01543
 
8.6%
81117
 
6.2%
9670
 
3.7%
10291
 
1.6%
15202
 
1.1%
ValueCountFrequency (%)
01543
8.6%
52107
11.8%
61682
9.4%
71606
9.0%
81117
6.2%
ValueCountFrequency (%)
15202
 
1.1%
132772
15.5%
125823
32.5%
11112
 
0.6%
10291
 
1.6%

mifid_next_year_savings
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.459804742
Minimum0
Maximum15
Zeros1543
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:51.941805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.990576165
Coefficient of variation (CV)0.4717101975
Kurtosis-0.6413946244
Mean8.459804742
Median Absolute Deviation (MAD)3
Skewness-0.4841732153
Sum151642
Variance15.92469813
MonotonicityNot monotonic
2021-05-31T16:09:52.169791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
123945
22.0%
133186
17.8%
52709
15.1%
62097
11.7%
71923
10.7%
01543
 
8.6%
81224
 
6.8%
9629
 
3.5%
10304
 
1.7%
15197
 
1.1%
ValueCountFrequency (%)
01543
8.6%
52709
15.1%
62097
11.7%
71923
10.7%
81224
6.8%
ValueCountFrequency (%)
15197
 
1.1%
133186
17.8%
123945
22.0%
11168
 
0.9%
10304
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
1
10837 
0
7088 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
110837
60.5%
07088
39.5%
2021-05-31T16:09:52.706209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:52.865178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
110837
60.5%
07088
39.5%

Most occurring characters

ValueCountFrequency (%)
110837
60.5%
07088
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
110837
60.5%
07088
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
110837
60.5%
07088
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
110837
60.5%
07088
39.5%

mifid_money_other_brokers
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
1
9685 
0
8240 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
19685
54.0%
08240
46.0%
2021-05-31T16:09:53.292215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:53.450218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
19685
54.0%
08240
46.0%

Most occurring characters

ValueCountFrequency (%)
19685
54.0%
08240
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
19685
54.0%
08240
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
19685
54.0%
08240
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
19685
54.0%
08240
46.0%

mifid_invested_other_brokers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.519665272
Minimum0
Maximum15
Zeros8241
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:53.594215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q312
95-th percentile13
Maximum15
Range15
Interquartile range (IQR)12

Descriptive statistics

Standard deviation5.532391874
Coefficient of variation (CV)1.002305684
Kurtosis-1.732952348
Mean5.519665272
Median Absolute Deviation (MAD)5
Skewness0.200839344
Sum98940
Variance30.60735985
MonotonicityNot monotonic
2021-05-31T16:09:53.815179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08241
46.0%
124640
25.9%
131573
 
8.8%
51190
 
6.6%
6804
 
4.5%
7638
 
3.6%
8439
 
2.4%
9227
 
1.3%
1083
 
0.5%
1549
 
0.3%
ValueCountFrequency (%)
08241
46.0%
51190
 
6.6%
6804
 
4.5%
7638
 
3.6%
8439
 
2.4%
ValueCountFrequency (%)
1549
 
0.3%
131573
 
8.8%
124640
25.9%
1141
 
0.2%
1083
 
0.5%

mifid_experience
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
9889 
1
8036 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
09889
55.2%
18036
44.8%
2021-05-31T16:09:54.344175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:54.505174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
09889
55.2%
18036
44.8%

Most occurring characters

ValueCountFrequency (%)
09889
55.2%
18036
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
09889
55.2%
18036
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
09889
55.2%
18036
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
09889
55.2%
18036
44.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
17300 
1
 
625

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%
2021-05-31T16:09:54.947174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:55.103215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%

Most occurring characters

ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
017300
96.5%
1625
 
3.5%

linked_account_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct126
Distinct (%)20.2%
Missing17300
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean35.8112
Minimum0
Maximum1033
Zeros301
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:55.322178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile231
Maximum1033
Range1033
Interquartile range (IQR)14

Descriptive statistics

Standard deviation102.5140369
Coefficient of variation (CV)2.862625014
Kurtosis30.41178242
Mean35.8112
Median Absolute Deviation (MAD)1
Skewness4.876431713
Sum22382
Variance10509.12776
MonotonicityNot monotonic
2021-05-31T16:09:55.727832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0301
 
1.7%
152
 
0.3%
230
 
0.2%
328
 
0.2%
414
 
0.1%
68
 
< 0.1%
108
 
< 0.1%
56
 
< 0.1%
175
 
< 0.1%
85
 
< 0.1%
Other values (116)168
 
0.9%
(Missing)17300
96.5%
ValueCountFrequency (%)
0301
1.7%
152
 
0.3%
230
 
0.2%
328
 
0.2%
414
 
0.1%
ValueCountFrequency (%)
10331
< 0.1%
8381
< 0.1%
7281
< 0.1%
6421
< 0.1%
6201
< 0.1%

has_demo_account
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
15221 
1
2704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%
2021-05-31T16:09:56.246864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:56.411858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%

Most occurring characters

ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
015221
84.9%
12704
 
15.1%

demo_account_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct166
Distinct (%)6.1%
Missing15221
Missing (%)84.9%
Infinite0
Infinite (%)0.0%
Mean13.56065089
Minimum0
Maximum1019
Zeros2056
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:56.628869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50.85
Maximum1019
Range1019
Interquartile range (IQR)0

Descriptive statistics

Standard deviation65.02690201
Coefficient of variation (CV)4.795264073
Kurtosis68.50048565
Mean13.56065089
Median Absolute Deviation (MAD)0
Skewness7.468444634
Sum36668
Variance4228.497985
MonotonicityNot monotonic
2021-05-31T16:09:56.920858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02056
 
11.5%
1109
 
0.6%
253
 
0.3%
443
 
0.2%
340
 
0.2%
531
 
0.2%
723
 
0.1%
621
 
0.1%
919
 
0.1%
1114
 
0.1%
Other values (156)295
 
1.6%
(Missing)15221
84.9%
ValueCountFrequency (%)
02056
11.5%
1109
 
0.6%
253
 
0.3%
340
 
0.2%
443
 
0.2%
ValueCountFrequency (%)
10191
< 0.1%
7921
< 0.1%
7581
< 0.1%
7291
< 0.1%
6711
< 0.1%

has_demo_trade
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
15787 
1
2138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%
2021-05-31T16:09:57.535590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:57.702582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%

Most occurring characters

ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
015787
88.1%
12138
 
11.9%

demo_trade_days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct224
Distinct (%)10.5%
Missing15787
Missing (%)88.1%
Infinite0
Infinite (%)0.0%
Mean27.92797007
Minimum0
Maximum1066
Zeros606
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:57.910597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q312.75
95-th percentile159.15
Maximum1066
Range1066
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation83.3932083
Coefficient of variation (CV)2.986010373
Kurtosis34.34469367
Mean27.92797007
Median Absolute Deviation (MAD)2
Skewness5.211646007
Sum59710
Variance6954.427191
MonotonicityNot monotonic
2021-05-31T16:09:58.250591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0606
 
3.4%
1327
 
1.8%
2169
 
0.9%
3115
 
0.6%
474
 
0.4%
755
 
0.3%
552
 
0.3%
652
 
0.3%
838
 
0.2%
1132
 
0.2%
Other values (214)618
 
3.4%
(Missing)15787
88.1%
ValueCountFrequency (%)
0606
3.4%
1327
1.8%
2169
 
0.9%
3115
 
0.6%
474
 
0.4%
ValueCountFrequency (%)
10661
< 0.1%
7921
< 0.1%
7411
< 0.1%
7291
< 0.1%
6711
< 0.1%

has_mock_account
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
17848 
1
 
77

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
017848
99.6%
177
 
0.4%
2021-05-31T16:09:58.823586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:09:59.002582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
017848
99.6%
177
 
0.4%

Most occurring characters

ValueCountFrequency (%)
017848
99.6%
177
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
017848
99.6%
177
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
017848
99.6%
177
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
017848
99.6%
177
 
0.4%

mock_account_days
Real number (ℝ≥0)

MISSING

Distinct42
Distinct (%)54.5%
Missing17848
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean71.90909091
Minimum0
Maximum635
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:09:59.208555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12
Q375
95-th percentile336.2
Maximum635
Range635
Interquartile range (IQR)74

Descriptive statistics

Standard deviation122.6312596
Coefficient of variation (CV)1.705365178
Kurtosis6.250862559
Mean71.90909091
Median Absolute Deviation (MAD)12
Skewness2.404544909
Sum5537
Variance15038.42584
MonotonicityNot monotonic
2021-05-31T16:09:59.527596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
013
 
0.1%
18
 
< 0.1%
25
 
< 0.1%
45
 
< 0.1%
33
 
< 0.1%
492
 
< 0.1%
2662
 
< 0.1%
112
 
< 0.1%
52
 
< 0.1%
1522
 
< 0.1%
Other values (32)33
 
0.2%
(Missing)17848
99.6%
ValueCountFrequency (%)
013
0.1%
18
< 0.1%
25
 
< 0.1%
33
 
< 0.1%
45
 
< 0.1%
ValueCountFrequency (%)
6351
< 0.1%
4141
< 0.1%
4011
< 0.1%
3931
< 0.1%
3221
< 0.1%

user_flow_name
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
2
13337 
0
4410 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2
ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%
2021-05-31T16:10:00.097554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:10:00.264555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%

Most occurring characters

ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
213337
74.4%
04410
 
24.6%
1178
 
1.0%

days_until_conversion_or_today
Real number (ℝ≥0)

Distinct1124
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.0414505
Minimum0
Maximum1123
Zeros102
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size140.2 KiB
2021-05-31T16:10:00.494559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q195
median348
Q3682
95-th percentile1012
Maximum1123
Range1123
Interquartile range (IQR)587

Descriptive statistics

Standard deviation334.8815129
Coefficient of variation (CV)0.8308860353
Kurtosis-0.9965547722
Mean403.0414505
Median Absolute Deviation (MAD)275
Skewness0.5145764274
Sum7224518
Variance112145.6277
MonotonicityNot monotonic
2021-05-31T16:10:00.807554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3164
 
0.9%
4153
 
0.9%
1150
 
0.8%
6142
 
0.8%
7139
 
0.8%
2132
 
0.7%
8122
 
0.7%
5121
 
0.7%
0102
 
0.6%
9102
 
0.6%
Other values (1114)16598
92.6%
ValueCountFrequency (%)
0102
0.6%
1150
0.8%
2132
0.7%
3164
0.9%
4153
0.9%
ValueCountFrequency (%)
11235
< 0.1%
11224
 
< 0.1%
112110
0.1%
11208
< 0.1%
11195
< 0.1%

is_converted
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.2 KiB
0
13608 
1
4317 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17925
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%
2021-05-31T16:10:01.440217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-31T16:10:01.609217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%

Most occurring characters

ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17925
100.0%

Most frequent character per category

ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common17925
100.0%

Most frequent character per script

ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17925
100.0%

Most frequent character per block

ValueCountFrequency (%)
013608
75.9%
14317
 
24.1%

Interactions

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Correlations

2021-05-31T16:10:01.889216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-31T16:10:02.677357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-31T16:10:03.478374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-31T16:10:04.274359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-31T16:10:04.955396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-31T16:09:42.219079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-31T16:09:44.451717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-31T16:09:45.108756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-31T16:09:45.609756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_money_other_brokersmifid_invested_other_brokersmifid_experiencehas_linked_accountlinked_account_dayshas_demo_accountdemo_account_dayshas_demo_tradedemo_trade_dayshas_mock_accountmock_account_daysuser_flow_namedays_until_conversion_or_todayis_converted
0EUR460.010.012.04.001313111210NaN0NaN0NaN0NaN221
1USD1010.011.011.04.038501700NaN0NaN0NaN0NaN221
2EUR460.00NaN0NaN0.011213111200NaN0NaN0NaN0NaN280
3USD430.011.00NaN0.0112510000NaN0NaN0NaN0NaN280
4USD81.00NaN0NaN0.01135111210NaN0NaN0NaN0NaN280
5EUR460.011.00NaN0.017811510NaN0NaN0NaN0NaN280
6EUR467.00NaN0NaN0.01137011200NaN0NaN0NaN0NaN280
7USD1610.00NaN0NaN0.011313111210NaN0NaN0NaN0NaN280
8USD80.00NaN0NaN0.011213011210NaN0NaN0NaN0NaN280
9EUR460.00NaN0NaN0.01121300000NaN0NaN0NaN0NaN280

Last rows

user_currencyuser_countrystart_mifid_dayshas_finished_mifidfinish_mifid_dayshas_depositfirst_deposit_daysfirst_deposit_amountfirst_deposit_platformmifid_actual_savingsmifid_next_year_savingsmifid_qualificationsmifid_money_other_brokersmifid_invested_other_brokersmifid_experiencehas_linked_accountlinked_account_dayshas_demo_accountdemo_account_dayshas_demo_tradedemo_trade_dayshas_mock_accountmock_account_daysuser_flow_namedays_until_conversion_or_todayis_converted
17915USD300.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211220
17916USD31206.01229.01380.02.45135111310NaN0NaN0NaN0NaN211220
17917USD3043.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211220
17918USD1601.0131.01393.02.0212121001187.0184.0184.00NaN23941
17919EUR460.011.013.02.05131310010NaN0NaN0NaN0NaN261
17920EUR4679.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211230
17921USD740.00NaN0NaN0.015710010NaN0NaN0NaN0NaN211230
17922EUR460.00NaN0NaN0.010000000NaN0NaN0NaN0NaN211230
17923EUR00.01260.00NaN0.0110600000NaN0NaN0NaN0NaN211230
17924GBP743.00NaN0NaN0.0171210010NaN0NaN0NaN0NaN211230